Browsing by Author "Lai, Patrick T. S."

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    Analyzing Chlamydia and Gonorrhea Health Disparities from Health Information Systems: A Closer Examination Using Spatial Statistics and Geographical Information Systems
    (2022-05) Lai, Patrick T. S.; Jones, Josette; Dixon, Brian E.; Wilson, Jeffrey; Wu, Huanmei; Shih, Patrick
    The emergence and development of electronic health records have contributed to an abundance of patient data that can greatly be used and analyzed to promote health outcomes and even eliminate health disparities. However, challenges exist in the data received with factors such as data inconsistencies, accuracy issues, and unstructured formatting being evident. Furthermore, the current electronic health records and clinical information systems that are present do not contain the social determinants of health that may enhance our understanding of the characteristics and mechanisms of disease risk and transmission as well as health disparities research. Linkage to external population health databases to incorporate these social determinants of health is often necessary. This study provides an opportunity to identify and analyze health disparities using geographical information systems on two important sexually transmitted diseases in chlamydia and gonorrhea using Marion County, Indiana as the geographical location of interest. Population health data from the Social Assets and Vulnerabilities Indicators community information system and electronic health record data from the Indiana Network for Patient Care will be merged to measure the distribution and variability of greatest chlamydia and gonorrhea risk and to determine where the greatest areas of health disparities exist. A series of both statistical and spatial statistical methods such as a longitudinal measurement of health disparity through the Gini index, a hot-spot and cluster analysis, and a geographically weighted regression will be conducted in this study. The outcome and broader impact of this research will contribute to enhanced surveillance and increased effective strategies in identifying the level of health disparities for sexually transmitted diseases in vulnerable localities and high-risk communities. Additionally, the findings from this study will lead to improved standardization and accuracy in data collection to facilitate subsequent studies involving multiple disparate data sources. Finally, this study will likely introduce ideas for potential social determinants of health to be incorporated into electronic health records and clinical information systems.
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    Assessment of Parkinson's Disease Progression by Feature Relevance Analysis and Regression Techniques Using Machine Learning Algorithms
    Gullapelli, Rakesh; Jones, Josette; Lai, Patrick T. S.
    Remote patient tracking has been gaining increased attention due to its low-cost non-invasive methods. Unified Parkinson's Disease Rating Scale (UPDRS) is used often to track Parkinson's Disease (PD) symptoms which requires the patient's visit to the clinic and time consuming medical tests that may not be feasible for most of the elderly PD patients. One of the major concerns to predict the PD in early stages is that PD symptoms overlap with the symptoms of other diseases such as Multiple Sclerosis, Alzheimer's disease. Moreover, most of the current methods used for tracking PD rely on expert clinical raters, from which PD symptoms assessment may be difficult due to inter-individual variability. Predicting relevant features using machine learning algorithms is helpful in providing the scientific decision-making classification rules necessary to assess the disease progression in early stages.
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    Automating Provider Reporting of Communicable Disease Cases using Health Information Technology
    (Office of the Vice Chancellor for Research, 2014-04-11) Dixon, Brian E.; Lai, Patrick T. S.; Kirbiyik, Uzay; Grannis, Shaun J.
    Introduction Disease surveillance is a core public health (PH) function, which enables PH authorities to monitor disease outbreak and develop programs and policies to reduce disease burden. To manage and adjudicate cases of suspected communicable disease, PH workers gather data elements about persons, clinical care, and providers from various clinical sources, including providers, laboratories, among others. Current processes are paper-based and often yield incomplete and untimely reporting across different diseases requiring time-consuming follow-up by PH authorities to get needed information. Health information technology (HIT) refers to a wide range of technologies used in health care settings, including electronic health records and laboratory information systems. Health information exchange (HIE) involves electronic sharing of data and information between HIT systems, including those used in PH. Previous research has shown that using HIE to electronically report laboratory results to PH can improve surveillance practice, yet there has been little utilization of HIE for improving provider-based disease reporting [1]. Methods Our study uses an intervention to electronically pre-populate provider-based communicable disease case reporting forms with existing clinical, laboratory and patient data available through one of the largest and oldest HIE infrastructures in the U.S., the Indiana Network for Patient Care. Evaluation of the intervention will be conducted utilizing mixed methods in a concurrent design framework in which qualitative methods are embedded within the quantitative methods. Quantitative data will include reporting rates, timeliness and burden and report completeness and accuracy, analyzed using interrupted time-series and other pre-post comparisons. Qualitative data regarding pre-post provider perceptions of report completeness, accuracy, and timeliness, reporting burden, data quality, benefits, utility, adoption, utilization and impact on reporting workflow will be collected using semi-structured interviews and open-ended survey items. Data will be triangulated to find convergence or agreement by cross-validating results to produce a contextualized portrayal of the facilitators and barriers to implementation and use of the intervention. Results The intervention has been implemented in seven primary care clinics in the metropolitan Indianapolis area plus one rural clinic in Edinburgh. Analysis of baseline data shows that provider-based reports vary in their completeness, yet they contain critical information not available from laboratory information systems [2]. Furthermore, PH workers access a range of sources to gather the data they need to investigate disease cases [3]. Discussion and Conclusion By applying mixed research methods and measuring context, facilitators and barriers, and individual, organizational and data quality factors that may impact adoption and utilization of the intervention, we will document whether and how the intervention streamlines provider-based manual reporting workflows, lowers barriers to reporting, increases data completeness, improves reporting timeliness and captures a greater portion of communicable disease burden in the community. Early results are promising, and continued evaluation will be completed over the next 24 months.
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    Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department
    (Springer Nature, 2017-06-23) Dixon, Brian E.; Zhang, Zuoyi; Lai, Patrick T. S.; Kirbiyik, Uzay; Williams, Jennifer; Hills, Rebecca; Revere, Debra; Gibson, P. Joseph; Grannis, Shaun J.; BioHealth Informatics, School of Informatics and Computing
    BACKGROUND: Most public health agencies expect reporting of diseases to be initiated by hospital, laboratory or clinic staff even though so-called passive approaches are known to be burdensome for reporters and produce incomplete as well as delayed reports, which can hinder assessment of disease and delay recognition of outbreaks. In this study, we analyze patterns of reporting as well as data completeness and timeliness for traditional, passive reporting of notifiable disease by two distinct sources of information: hospital and clinic staff versus clinical laboratory staff. Reports were submitted via fax machine as well as electronic health information exchange interfaces. METHODS: Data were extracted from all submitted notifiable disease reports for seven representative diseases. Reporting rates are the proportion of known cases having a corresponding case report from a provider, a faxed laboratory report or an electronic laboratory report. Reporting rates were stratified by disease and compared using McNemar's test. For key data fields on the reports, completeness was calculated as the proportion of non-blank fields. Timeliness was measured as the difference between date of laboratory confirmed diagnosis and the date the report was received by the health department. Differences in completeness and timeliness by data source were evaluated using a generalized linear model with Pearson's goodness of fit statistic. RESULTS: We assessed 13,269 reports representing 9034 unique cases. Reporting rates varied by disease with overall rates of 19.1% for providers and 84.4% for laboratories (p < 0.001). All but three of 15 data fields in provider reports were more often complete than those fields within laboratory reports (p <0.001). Laboratory reports, whether faxed or electronically sent, were received, on average, 2.2 days after diagnosis versus a week for provider reports (p <0.001). CONCLUSIONS: Despite growth in the use of electronic methods to enhance notifiable disease reporting, there still exists much room for improvement.
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    Evaluating the Completeness of Data Elements of Provider Reporting on Indiana's Communicable Disease Reports
    (Office of the Vice Chancellor for Research, 2014-04-11) Lai, Patrick T. S.; Gujjula, Kavya; Grannis, Shaun J.; Dixon, Brian E.
    Objective To examine the completeness of data elements required for notifiable disease surveillance from official, provider-based reports submitted to a local health department. Introduction Completeness of public health information is essential for the accurate assessment of community health progress and disease surveillance. Yet challenges persist with respect to the level of completeness that public health agencies receive in reports submitted by health care providers. Missing and incomplete data can jeopardize information reliability and quality resulting in inaccurate disease evaluation and management (1). Additionally, incomplete data can prolong the time required for disease investigators to complete their work on a reported case. Thus, it is important to determine where the scarcity of information is coming from to recognize the characteristics of provider reporting. Methods Data from 1,195 unique patient cases across 7 notifiable diseases were abstracted from official reporting forms (2) submitted to a local health department serving the Indianapolis metropolitan area. The selected diseases were chlamydia, gonorrhea, syphilis, salmonella, histoplasmosis, hepatitis B-acute, and hepatitis C-chronic. Table 1 represents the duration and collection period for each of the selected diseases. Diseases were purposely chosen to represent the broad range managed by local health departments. Diseases were purposely chosen to represent the broad range managed by local health departments. A set of data elements consisting of patient, clinical, and provider information was then evaluated for completeness. The level of completeness was determined using a classification method similar to that used by Dixon et al. (3). Fields were considered complete if they contained a value; the recorded value was not validated for accuracy. Results Table 2 depicts the level of completeness for the selected data elements across the target diseases. Completeness levels and percentages varied by disease and data element with completeness being higher for patient demographic information (e.g., name, address) than provider demographics (e.g., name, clinic address). The majority of data elements for patient demographics were categorized as mostly to always complete. Conclusion It is essential that provider reports are completed in a thorough and timely manner. To increase documentation of provider information, analyses of provider characteristics such as workflow patterns, organizational constraints, and information needs are essential to understand the completeness level of provider information reporting. This will allow us to develop implementation of strategies to increase completeness of reporting across all data elements necessary to assess and investigate notifiable diseases.